Explainable Graph Representation Learning via Graph Pattern Analysis
Authors: Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on the TUdataset [Morris et al., 2020] for both supervised and unsupervised learning tasks, as shown in Table 1. Our goal is to learn explainable graph representations. We provide the weight parameter λ and visualize the ensemble representation g and the pattern representation z(m). We use seven graph patterns: paths, trees, graphlets, cycles, cliques, wheels, and stars, sampling Q = 10 subgraphs for each. We select these patterns based on their discriminative power and computational feasibility. In practice, one could use a subset of these seven patterns and adjust the sampling cardinality Q based on domain knowledge or computational constraints. We use a 5-layer GCN for the representation learning function F and a 3-layer DNN with softmax for the classification function fc. Experiments are repeated ten times and the average value and standard deviation are reported. Due to the space limitation, the results of PXGL-EGK and other figures are shown in the supplementary materials. |
| Researcher Affiliation | Academia | 1School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China 2Shenzhen Research Institute of Big Data, Shenzhen, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in narrative text and mathematical formulations. There are no explicit sections or figures labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of open-source code, nor does it include any links to code repositories. |
| Open Datasets | Yes | We test our method on the TUdataset [Morris et al., 2020] for both supervised and unsupervised learning tasks, as shown in Table 1. |
| Dataset Splits | Yes | The dataset is split into 80% training, 10% validation, and 10% testing data. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, or cloud computing instances with detailed specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as libraries or frameworks with their version numbers, that were used to implement and run the experiments. |
| Experiment Setup | Yes | We use seven graph patterns: paths, trees, graphlets, cycles, cliques, wheels, and stars, sampling Q = 10 subgraphs for each. ... We use a 5-layer GCN for the representation learning function F and a 3-layer DNN with softmax for the classification function fc. Experiments are repeated ten times and the average value and standard deviation are reported. The dataset is split into 80% training, 10% validation, and 10% testing data. |